ECG-based Mental Stress Assessment Using Fuzzy Computing and Associative Petri Net

Abstract

Medical reports suggest that long-term stress can directly or indirectly lead to mental disorders and cardiovascular disease. However, many people frequently ignore their stress symptoms and fail to take remedial action before developing serious psychological or physiological health problems. Many previous studies have used electrocardiograms (ECG) to evaluate mental stress. However, ECG pattern recognition presents difficulties because the time-varying morphology is subject to physiological conditions and the presence of noise. Therefore, how to achieve effective noise reduction and accurately determine an individual’s exposure to mental stress under various conditions is a recurring issue in engineering and medical research. Heart rate variability is assessed using time- and frequency-domain analyses. To accurately detect mental stress, this research adopts both time and frequency domains, two aspects of physiological characteristics. In this study, a rule-based reasoning model is created for mental stress assessment by combining fuzzy and associative Petri net methodologies. It can serve as a basis for clinical diagnosis and quickly measure the subject’s mental condition and reduce subjective errors. Performance evaluation using Physionet’s stress recognition database shows that the proposed approach compares well with data mining methods and other proposed methods. Moreover, a prototype mental stress assessment system is proposed to help users understand their mental condition.